Algorithms’ performance in the simulation benchmark was assessed by their sensitivity and precision. The sensitivity was calculated as the fraction of genes with adjusted P value <0.1 among the genes with true differences between group means. The precision was calculated as the fraction of genes with true differences between group means among those with adjusted P value <0.1. The sensitivity is plotted over 1−precision, or the FDR, in Figure 6. DESeq2, and also edgeR, often had the highest sensitivity of the algorithms that controlled type-I error in the sense that the actual FDR was at or below 0.1, the threshold for adjusted P values used for calling differentially expressed genes. DESeq2 had higher sensitivity compared to the other algorithms, particularly for small fold change (2 or 3), as was also found in benchmarks performed by Zhou et al. [34]. For larger sample sizes and larger fold changes the performance of the various algorithms was more consistent.
